class_docstr: DEPRECATED: Please use `Evaluator` instead.
  
  The model validator helps prevent bad models from being pushed to production.
  It does this by validating exported models against known good models (e.g. the
  current production model), and marking the exported model as good ("blessing
  it") only if the exported model's metrics are within predefined thresholds
  around the good model's metrics.
  
  The model validator will validate tf.serving format exported models produced
  by the Trainer component. The validator evaluates the models on examples
  created by the ExampleGen component. The validator will also automatically
  read data written by the Pusher component regarding the latest pushed models
  by using ml.metadata to query the previously pushed artifacts.
  
  To include ModelValidator in a TFX pipeline, configure your pipeline similar
  to
  https://github.com/tensorflow/tfx/blob/master/tfx/examples/chicago_taxi_pipeline/taxi_pipeline_simple.py#L110.
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docstr: Check threshold.
docstr_tok: ["Check","threshold","."]
code: def _pass_threshold(self, eval_result: tfma.EvalResult) -> bool:
      """Check threshold."""
      return True
  
file_path: tfx/tfx/components/model_validator/executor.py
imports: import os
  from typing import Any, Dict, List
  import absl
  import apache_beam
  import tensorflow_model_analysis
  from tfx import types
  from tfx.components.model_validator import constants
  from tfx.dsl.components.base import base_beam_executor
  from tfx.dsl.io import fileio
  from tfx.types import artifact_utils
  from tfx.utils import io_utils
  from tfx.utils import path_utils
label_desc: Use image node_id 1 for calling the Executor obj's underlying member method code with example usage: obj._pass_threshold(eval_result) and returns: True